Due to strong needs of improved audio-visual environments, a television system that has a higher resolution than the conventional systems was desired. As a result, a so-called high-vision system was developed. The number of scanning lines of the high-vision system (1125 lines) is more than twice of the number of scanning lines of the so-called NTSC system (525 lines). In addition, the aspect ratio of the display screen of the high-vision system (9:16) is a wide-angle more than the aspect ratio of the display screen of the NTSC system (3:4). Thus, the high-vision system provides the users with high-resolution and sense of presence.
The high-vision system, which has such excellent characteristics, cannot directly display a picture with an NTSC picture signal due to a difference of their standards. Thus, to display an NTSC picture signal on a display of the high-vision system, the rate of the picture signal is converted with a picture information converting apparatus as shown in FIG. 20.
In FIG. 20, the conventional picture information converting apparatus comprises a horizontal interpolating filter 152 and a vertical interpolating filter 153. The horizontal interpolating filter 152 horizontally interpolates an NTSC picture signal (SD data) received from an input terminal 151. The vertical interpolating filter 153 vertically interpolates the picture signal that has been horizontally interpolated.
In reality, the horizontal interpolating filter 152 has a structure as shown in FIG. 21. In the example shown in FIG. 21, the horizontal interpolating filter 152 is composed of a cascade-connected FIR filter. In FIG. 21, reference numeral 161 is an input terminal to which SD data is supplied. Reference numerals 162.sub.0 to 162.sub.m are multiplying devices that multiply SD data by filter coefficients .alpha..sub.0 to .alpha..sub.m, respectively. Reference numerals 163.sub.0 to 163.sub.m-1 are adding devices. Reference numerals 164.sub.1 to 164.sub.m-1 are delay devices by time T (where T is one sampling period). Output data that has been horizontally interpolated is supplied from an output terminal 165. The output data is supplied to the vertical interpolating filter 153.
The vertical interpolating filter 153 has the similar structure to the horizontal interpolating filter 152. The vertical interpolating filter 153 interpolates pixels in the vertical direction so as to vertically interpolate pixels of the NTSC picture signal that have been horizontally interpolated. The resultant high-vision picture signal (HD data) is supplied to a high-vision receiver. Thus, the high-vision receiver can display a picture corresponding to the NTSC picture signal.
However, the conventional picture information converting apparatus simply interpolates pixels in the horizontal and vertical directions corresponding to the NTSC picture signal. Thus, the resolution of the resultant signal that has been horizontally and vertically interpolated is the same as that of the original NTSC picture signal. In particular, when a normal picture is converted, it is normally interpolated in the vertical direction in the field thereof. In this case, since fields of the picture are not correlated, due to a conversion loss in still picture portions, the resolution of the resultant picture signal becomes lower than that of the NTSC picture signal.
To solve such a drawback, the applicant of the present patent application has proposed a picture signal converting apparatus (as Japanese Patent Application No. HEI 6-205934) that categorizes a picture signal level of an input signal as a class corresponding to a three-dimensional (temporal and spatial) distribution thereof, stores a prediction coefficient value that has been learnt corresponding to each class in a storing means, and outputs an optimum estimation value corresponding to a prediction equation.
In the method used in the apparatus, when HD (High Definition) pixels are created, relevant SD (Standard Definition) pixel data is categorized as a class. A prediction coefficient value for each class is learnt beforehand. Pixel data in a still picture portion is correlated in the frame. Pixel data in a moving picture portion is correlated in the field. Thus, HD data that is similar to a true picture signal of a still picture is skillfully obtained.
For example, to generate HD pixels y.sub.1 to y.sub.4 as shown in FIGS. 2 and 3, the average value of differences of SD pixels m.sub.1 to m.sub.5 and SD pixels n.sub.1 to n.sub.5 shown in FIG. 5 at the same spatial position of different frames are calculated. The calculated average value is categorized as a class with a threshold value so as to represent a moving degree.
Likewise, as shown in FIG. 4, SD pixels k.sub.1 to k.sub.5 are processed by the ADRC (Adaptive Dynamic Range Coding) technique. Thus, the picture signal is categorized as a class with a small number of bits so as to represent a waveform in the space.
With SD pixels x.sub.1 to x.sub.25 as shown in FIG. 9, linear equations are created corresponding to the individual classes categorized by the above-described two types of class categorizations as so as to learn and obtain prediction coefficient values. In this system, classes that represent the moving degree and the waveform in the space are separately and adequately categorized. Thus, with a relatively small number of classes, high conversion characteristics can be obtained. A HD pixel y is predicted with prediction coefficient values w.sub.n obtained as described above corresponding to the following formula (1). EQU y=w.sub.1 x.sub.1 +w.sub.2 x.sub.2 + . . . +w.sub.n x.sub.n (1)
where n=25.
As described above, predication coefficient values for predicting individual HD data corresponding to SD data are learnt and obtained beforehand. The resultant prediction coefficient values are stored in a ROM table. By outputting SD data and prediction coefficient values that have been read from the ROM table, data similar to real HD data can be output unlike with data of which input SD data is simply interpolated.
Next, with reference to FIG. 22, a real operation of the prior art reference will be described. SD pixel data is received from an input terminal 171. The SD pixel data is supplied to area extracting circuits 172, 174, and 178. The area extracting circuit 172 extracts SD pixels k.sub.1 to k.sub.5 as shown in FIG. 4 so as to perform a class categorization that represents a waveform in the space. An ADRC circuit 173 performs the ADRC process. The area extracting circuit 174 extracts SD pixels m.sub.1 to m.sub.5 and SD pixels n.sub.1 to n.sub.5 as shown in FIG. 5 so as to perform a class categorization that represents a moving degree of the pixels. A moving class determining circuit 175 calculates the average value of differences of pixels at the same position among frames in the space, limits the average value with a predetermined threshold value, and categorizes the resultant value as a class.
A class code generating circuit 176 generates a class corresponding to the class received from the ADRC circuit 173 and the class received from the moving class determining circuit 175. A ROM table 177 reads a prediction coefficient corresponding to the generated class. The area extracting circuit 178 extracts SD pixels x.sub.1 to x.sub.25 as shown in FIG. 9 and supplies them to a prediction calculating circuit 179. The prediction calculating circuit 179 outputs HD data corresponding to the liner equation expressed by the formula (1) through an output terminal 180.
FIG. 23 shows a sum-of-products calculating circuit for use with such a picture signal converting apparatus. A multiplicand register 191 supplies a plurality of SD data to a sum-of-products calculating device 192. An address controlling circuit 193 supplies class codes class corresponding to the SD data to a multiplier memory 194. The multiplier memory 194 supplies coefficient data corresponding to the class codes class to the sum-of-products calculating device 192. The sum-of-products calculating device 192 calculates the sum of products of SD data and coefficient data. The resultant sum-of-products data is supplied from an output terminal 195.
As an example of the sum-of-products calculating circuit 192, as shown in FIG. 24, SD data is received from an input terminal 201. The SD data is supplied to a multiplying device 205 through a register 202. Coefficient data is received from an input terminal 203. The coefficient data is supplied to a multiplying device 205 through a register 204. The multiplying device 205 multiplies the SD data by the coefficient data. The multiplied output is supplied to an adding device 207 through a register 206. The adding device 207 adds the two multiplied outputs. An output of the adding device 207 is supplied to an adding device 209 through a register 208. The adding device 209 adds two added outputs. An output of the adding device 209 is supplied from an output terminal 211 through a register 210.
In operations with the sum-of-products calculating circuit, multipliers (coefficient data) are stored in a memory or the like beforehand. Corresponding to characteristics of a picture (namely, class information), multipliers are varied. Such a structure has been used for converting picture signals.
In the class categorizing picture information converting process, as the number of pixels used for the prediction calculation increases, the converting performance improves. In other words, as the value n in the formula (1) increases, the converting performance improves. Generally speaking, the converting performance is proportional to the number of taps of a filter.
However, when a converting apparatus of which the value n in the equation (1) is large is fabricated, the circuit scales of the ROM table that stores coefficients and of the circuit that performs the prediction calculation become large.
In addition, when the number of classes is increased, the capacity of the multiplier memory increases corresponding to the number of types of multipliers. Thus, the hardware scale increases.
As described above, it is very difficult to structure the process for converting class categorized picture information with high conversion performance in a small-scale at low cost.